ArticleBMC medical informatics and decision making2016
Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE).
Article in BMC medical informatics and decision making, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.Frontiers in public health · 2026Article
- Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision.Briefings in bioinformatics · 2024Review
- Privacy-preserving federated genome-wide association studies via dynamic sampling.Bioinformatics (Oxford, England) · 2023Article
- Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation.BMC bioinformatics · 2022Article
- New Approach to Privacy-Preserving Clinical Decision Support Systems for HIV Treatment.Journal of medical systems · 2022Article
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- Privacy-preserving logistic regression with secret sharing.BMC medical informatics and decision making · 2022Article
- Accurate training of the Cox proportional hazards model on vertically-partitioned data while preserving privacy.BMC medical informatics and decision making · 2022Article
- sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies.Genome biology · 2022Article
- Privacy-preserving dataset combination and Lasso regression for healthcare predictions.BMC medical informatics and decision making · 2021Article
- Privacy-preserving data sharing infrastructures for medical research: systematization and comparison.BMC medical informatics and decision making · 2021Article
- Weight-Based Framework for Predictive Modeling of Multiple Databases With Noniterative Communication Without Data Sharing: Privacy-Protecting Analytic Method for Multi-Institutional Studies.JMIR medical informatics · 2021Article
- The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm.JAMIA open · 2020Article
- EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Secure top most significant genome variants search: iDASH 2017 competition.BMC medical genomics · 2018Article
- Linkage of national soil quality measurements to primary care medical records in England and Wales: a new resource for investigating environmental impacts on human health.Population health metrics · 2018Article
- Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis.JMIR medical informatics · 2018Article
- Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.JMIR medical informatics · 2018Article
- PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension.BMC medical genomics · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundIn biomedical research, data sharing and information exchange are very important for improving quality of care, accelerating discovery, and promoting the meaningful secondary use of clinical data. A big concern in biomedical data sharing is the protection of patient privacy because inappropriate information leakage can put patient privacy at risk.
methodsIn this study, we deployed a grid logistic regression framework based on Secure Multi-party Computation (SMAC-GLORE). Unlike our previous work in GLORE, SMAC-GLORE protects not only patient-level data, but also all the intermediary information exchanged during the model-learning phase.
resultsThe experimental results demonstrate the feasibility of secure distributed logistic regression across multiple institutions without sharing patient-level data.
conclusionsIn this study, we developed a circuit-based SMAC-GLORE framework. The proposed framework provides a practical solution for secure distributed logistic regression model learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.